id name fed sex
Min. : 100013 Length:201015 Length:201015 Length:201015
1st Qu.: 2842263 Class :character Class :character Class :character
Median :14542960 Mode :character Mode :character Mode :character
Mean :22859665
3rd Qu.:35829558
Max. :94799962
title wtitle otitle foa
Length:201015 Length:201015 Length:201015 Length:201015
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
rating games k bday
Min. :1400 Min. : 0.000 Min. :10.00 Min. :1900
1st Qu.:1564 1st Qu.: 0.000 1st Qu.:20.00 1st Qu.:1973
Median :1715 Median : 0.000 Median :40.00 Median :1998
Mean :1745 Mean : 1.701 Mean :30.73 Mean :1991
3rd Qu.:1886 3rd Qu.: 0.000 3rd Qu.:40.00 3rd Qu.:2009
Max. :2839 Max. :45.000 Max. :40.00 Max. :2021 STAT 6179 | PROJECT
Analysis of Chess Player Ratings
Eldor Rakhmonaliev 48756997
Faculty of Science and Engineering
Content
- INTRODUCTION
- RESEARCH QUESTIONS
- DATASET DESCRIPTION
- RESULTS AND INTERPRETATION
- KEY FINDINGS AND CONCLUSION
- REFERENCES
INTRODUCTION AND RESEARCH QUESTIONS
INTRODUCTION
Overview
This report presents key findings from the analysis of chess player ratings for August and September 2025, focusing on player performance, rating changes, and trends.
The study examines how changes in rating points affect overall player outcomes and explores potential differences in ratings between genders. By investigating these aspects, the analysis provides insights into player development, rating dynamics, and trends that may help forecast future performance.
Research Questions
Do chess players’ ratings improve or decline over time, and does this vary by player strength or activity level?
Is there a gender gap in chess rating performance and improvement rates?
Does federation size predict improvement success? Do smaller federations outperform larger ones in rating growth?
DATASET DESCRIPTION
DATASET DESCRIPTION
The dataset consists of two files: fide_ratings_august and fide_ratings_september, obtained from TidyTuesday.
These datasets contain player ratings and additional information about individual chess players.
Summary of August Ratings File :
Summary of September Ratings File :
id name fed sex
Min. : 100013 Length:203191 Length:203191 Length:203191
1st Qu.: 2849372 Class :character Class :character Class :character
Median :14576805 Mode :character Mode :character Mode :character
Mean :23022064
3rd Qu.:36045730
Max. :94799962
title wtitle otitle foa
Length:203191 Length:203191 Length:203191 Length:203191
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
rating games k bday
Min. :1400 Min. : 0.000 Min. :10.0 Min. :1900
1st Qu.:1563 1st Qu.: 0.000 1st Qu.:20.0 1st Qu.:1973
Median :1713 Median : 0.000 Median :40.0 Median :1998
Mean :1743 Mean : 1.619 Mean :30.8 Mean :1991
3rd Qu.:1884 3rd Qu.: 0.000 3rd Qu.:40.0 3rd Qu.:2009
Max. :2839 Max. :40.000 Max. :40.0 Max. :2021 Dataset after Merging and Column Transformation :
| ID | Name | Federation | Sex | Birthday | August_Rating | August_Games | September_Rating | September_Games | Rating_Change | Games_Played | Age |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1503014 | Carlsen, Magnus | NOR | M | 1990 | 2839 | 0 | 2839 | 0 | 0 | 0 | 35 |
| 2016192 | Nakamura, Hikaru | USA | M | 1987 | 2807 | 0 | 2807 | 0 | 0 | 0 | 38 |
| 2020009 | Caruana, Fabiano | USA | M | 1992 | 2784 | 0 | 2789 | 9 | 5 | 9 | 33 |
| 25059530 | Praggnanandhaa R | IND | M | 2005 | 2779 | 0 | 2785 | 9 | 6 | 9 | 20 |
| 35009192 | Erigaisi Arjun | IND | M | 2003 | 2776 | 0 | 2771 | 9 | -5 | 9 | 22 |
RESULTS AND INTERPRETATION
RATING DISTRIBUTION (RQ1)
GAMES VS. RATING CHANGE (RQ1)
GENDER ANALYSIS: OVERVIEW (RQ2)
Across August and September 2025:
Key Stats: Male: 351,742 | Female: 40,158 | Avg rating gap: 114 points
GENDER GAP ACROSS RATING LEVELS (RQ2)
GENDER & ACTIVITY PATTERNS (RQ2)
FEDERATION CLUSTERS (RQ3)
KEY FINDINGS AND CONCLUSION
KEY FINDINGS AND CONCLUSION
Activity Impact
- Active players show greater rating volatility
- Inactive players maintain stable ratings
- Playing regularly introduces both risk and opportunity
Rating Level Patterns
- Elite players (2500+) show smallest fluctuations
- Lower-rated players experience larger swings
- Rating stability increases with strength
Games vs. Improvement
- Weak correlation between games and rating gain
- Quality of opponents matters more than quantity
- Some players improve significantly with few games
Federations Performance
- Not all Federations with more player have good average ratings
- There are some federations with small players but high ratings
REFERENCES
REFERENCES
Fung, Thomas. 2025. “Quarto-Mq-Revealjs-Theme: Macquarie University Quarto Reveal.js Presentation Template.” https://github.com/thomas-fung/quarto-mq-revealjs-theme.
Pedersen, Thomas Lin. 2025. Patchwork: The Composer of Plots. https://CRAN.R-project.org/package=patchwork.
R for Data Science. 2025. “TidyTuesday: FIDE Chess Ratings – Week of 2025-09-23.” https://github.com/rfordatascience/tidytuesday/tree/main/data/2025/2025-09-23.
Sievert, Carson, Chris Parmer, Toby Hocking, Scott Chamberlain, Jeff Allen, and Hadley Wickham. 2025. Plotly: Create Interactive Web Graphics via ’Plotly.js’. https://CRAN.R-project.org/package=plotly.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2025. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
STAT6179 | Eldor Rakhmonaliev 48756997